State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast Online publication date: Fri, 02-Jun-2006
by Rui Yamaguchi, Tomoyuki Higuchi
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 1, No. 1, 2006
Abstract: We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state variables in a system. To identify the system, we estimate parameters of the model by EM algorithm and determine the dimension of the state variable by BIC.
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